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4 cards from this section. Quick, good the night before a test.
What does CNN stand for?
Think about networks used for image processing.
Name a common use case for RNNs.
Consider applications involving sequences.
What type of data do CNNs primarily handle?
Think about the data formats that CNNs are designed for.
True or False: LSTMs can remember information for longer sequences than standard RNNs.
Consider the capabilities of LSTMs.
How would you approach building a model for classifying handwritten digits? Which architecture would you choose and why?
Think about the nature of data you are working with.
You need to predict future stock prices based on historical data. Which architecture is more suitable, and how would you implement it?
Consider how the architecture's memory feature benefits time-based predictions.
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